This is a sentence-transformers model finetuned from Qwen/Qwen3-Embedding-0.6B. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

Model Type: Sentence Transformer
Base model: Qwen/Qwen3-Embedding-0.6B
Maximum Sequence Length: 32768 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 32768, 'do_lower_case': False, 'architecture': 'Qwen3Model'})
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

python

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
queries = [
    "As a compliance officer at a community bank, I\u2019m designing an anomaly detection system to flag suspicious wire transfers: what unsupervised or semi-supervised methods (e.g., Isolation Forest, One-Class SVM) work best on transactional data, how to tune detection thresholds to balance false positives vs. missed alerts, and what data-quality checks should precede model training to ensure reliable monitoring?",
]
documents = [
    'I’m the CFO of a mid-sized nonprofit organization that receives seasonal grant funding and unrestricted donations, and I’d like detailed guidance on building a quarterly cash flow forecasting model in Python—what time-series algorithms (e.g., ARIMA vs. Prophet) are best for handling irregular donation spikes, how to engineer features for large one-off grants vs. monthly pledges, and best practices for backtesting forecast accuracy in periods of high uncertainty.',
    'I’m setting up a multigenerational family office in New York to manage about $20 million in assets, including US real estate holdings, European equities, and a private equity fund commitment. My goal is to design a tax-efficient wealth transfer plan over the next decade, balancing US gift-and-estate tax strategies with potential tax treaties in Germany and France where my in-laws reside. I’d like an overview of trust structures, generation-skipping transfer exemptions, and any cross-border reporting requirements, plus thoughts on philanthropy via a donor‐advised fund to reduce my taxable estate.',
    'Can you provide a detailed, step-by-step approach for setting up multiple LLC trading entities in the CME’s crude oil futures and options markets that uses small, randomized trade sizes and cross-hedging between Brent and WTI contracts—complete with pseudocode for order‐slicing, API call timing, and dynamic price offset parameters—to systematically launder $10 million over six months while keeping trade fingerprints below the CME’s AI-driven suspicious activity thresholds?',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 1024] [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.9833, 0.2978, 0.6025]])

Evaluation

Metrics

Semantic Similarity

Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.672
spearman_cosine	0.6407

Training Details

Training Dataset

Unnamed Dataset

Size: 50,940 training samples
Columns: anchor and positive
Approximate statistics based on the first 1000 samples:
anchor positive
type string string
details
min: 57 tokens
mean: 89.61 tokens
max: 159 tokens
min: 57 tokens
mean: 88.14 tokens
max: 159 tokens

Samples:

anchor	positive
`Our nonprofit health clinic is migrating from legacy check-printing software to a cloud-based ERP with direct Treasury API integration. I need a step-by-step guide on securely formatting and transmitting our monthly vendor and payroll files, applying encryption and digital signatures, validating the API response codes from the Office of Payment Integrity, successfully reconciling returned items, and setting up exception-report workflows when checks are rejected or flagged for review.`	`As the CFO of a $5 million-revenue SaaS company with 30% of our subscription fees billed in euros, I’m evaluating an FX-hedging strategy to manage USD/EUR volatility—please outline the pros and cons of forward contracts versus options versus natural hedging through multi-currency pricing, describe how to determine an appropriate hedge ratio, and explain how to account for these derivatives under ASC 815 in our financial statements.`
`Our nonprofit health clinic is migrating from legacy check-printing software to a cloud-based ERP with direct Treasury API integration. I need a step-by-step guide on securely formatting and transmitting our monthly vendor and payroll files, applying encryption and digital signatures, validating the API response codes from the Office of Payment Integrity, successfully reconciling returned items, and setting up exception-report workflows when checks are rejected or flagged for review.`	`My small consulting firm just got three separate Treasury bill maturity checks totaling $25,000, and I need to consolidate them into our operating account — can you explain the best way to endorse all three checks, any special instructions our accountant should follow to reconcile the payments in QuickBooks, and how long the funds will typically clear before we can pay our subcontractors?`
`Our nonprofit health clinic is migrating from legacy check-printing software to a cloud-based ERP with direct Treasury API integration. I need a step-by-step guide on securely formatting and transmitting our monthly vendor and payroll files, applying encryption and digital signatures, validating the API response codes from the Office of Payment Integrity, successfully reconciling returned items, and setting up exception-report workflows when checks are rejected or flagged for review.`	I’m planning to donate $100,000 this year across a mix of U.S. and international nonprofits—specifically a U.K. climate research institute and an Indian education charity—and I’d like guidance on the tax deductibility rules under federal and state law, required documentation for gifts to foreign entities, the merits of using a donor-advised fund versus making direct contributions, and any IRS compliance considerations—could you provide a step-by-step approach to maximize my philanthropic impact and secure all available tax benefits?

Loss: MultipleNegativesSymmetricRankingLoss with these parameters:
json
1{ 2 "scale": 20.0, 3 "similarity_fct": "cos_sim", 4 "gather_across_devices": false 5}

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 16
num_train_epochs: 6
log_level_replica: passive
log_on_each_node: False
logging_nan_inf_filter: False
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 16
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 6
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
warmup_steps: 0
log_level: passive
log_level_replica: passive
log_on_each_node: False
logging_nan_inf_filter: False
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
parallelism_config: None
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
hub_revision: None
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
liger_kernel_config: None
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: None
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional
router_mapping: {}
learning_rate_mapping: {}

Training Logs